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Hi Ian,

thanks for very detailed reply!

>> - do you think it is better than looking at three values {map CC, 2mFo-DFc,
>> mFo-DFc} and why?
>>     
>
> Yes, because all the information you need is encapsulated in 1 number
> per region of interest!  

I agree it's a good reason.

> But I don't understand what you mean by
> 2mFo-DFc & mFo-DFc being counted each as 1 number.  

Given the map and model, you can get the map value at (x,y,z) position, 
for example, at the center of atom. This is what phenix.model_vs_data 
reports. For each atom you get three numbers: map CC, and the values of 
2mFo-DFc and mFo-DFc maps at the atomic position.

>> By suggesting to use {map CC, 2mFo-DFc, mFo-DFc} I was assuming that:
>> - map CC will tell you about similarities of shapes and it will not tell you
>> about how strong the density is, indeed.  So, using map CC alone is clearly
>> insufficient. Also, we more or less have feeling about the values, which is
>> helpful.
>> - 2mFo-DFc will tell you about the strength of the density. I mean, if you
>> get 2.5sigma at the center of atom A -  it's good (provided that map CC is
>> good), and if it is 0.3sigma you should get puzzled.
>> - Having excess of +/- mFo-DFc density will tell you something too.
>>     
>
> The problem is how is all this information quantified in an objective
> and statistically justifiable way in order to arrive at a firm
> conclusion?
>   

We can more or less relate these values to the map appearance and 
model-to-map fit quality. Looking at these numbers one can approximately 
tell whether it's good, so so, or bad. It's like crystallographic 
reciprocal space R-factor. If I see R=35% for a structure at 2A 
resolution - it's not good, and R=17% is much better.

Anyway I will code that formula and play with it.

Thanks again!
Pavel.